Wednesday is an agent-infrastructure day with a price cut on top. The lead is OpenAI splitting GPT-6 into Sol and Luna at half the GPT-5.6 promotional prices, live in ChatGPT Work and Codex — and the durable part is the caching change underneath, not the discount. Everything around that lead is the unglamorous machinery agents now run on: compaction that halves coding-agent cost inside a bounded window, the sandbox platform serving 3M agent environments a day, an MCP supply-chain attack that lands at 93.6%, and a paper showing local tool-call fidelity may be measuring your serving stack rather than your model. Also today: a self-improvement loop that ran unattended for eight days, the open-weights balance of power in Nathan Lambert’s Congressional testimony, and Bloomberg’s probe into AI’s role in the Minab school strike.

GPT-6 Sol and Luna — OpenAI splits the flagship and halves API prices

  • Introducing GPT-6 Sol and Luna — two new GPT-6 tiers ship at half their GPT-5.6 promotional prices, live in ChatGPT Work and Codex as gpt-6-sol and gpt-6-luna.
    • Prices: Sol $2/$10 per M tokens · Luna $0.10/$0.50 · Astra stays the flagship.
    • AutomationBench (Zapier’s 47-tool suite): Sol 33.2% vs Opus 5’s 26.9%, at 9% of Opus 5’s cost per task.
    • DeepSWE v1.1: Sol 68.8% against Fable 5’s 69.9%, ~80% cheaper per task; Luna 66.6% at 93-96% less.
    • The part that outlives the launch: 90% off cached reads, explicit cache breakpoints, and reasoning-effort or tool changes that don’t invalidate the prefix.
    • OpenAI says GitHub now processes >50% fewer fresh prompt tokens across billions of requests.
    • Flag: every comparison is OpenAI-run, competitor scores come from published reports, and AutomationBench is a third party’s benchmark. (HN 1559 · 750c · OpenAI)

Agent frameworks & tooling

  • CliffCompaction: Cost-Efficient Compaction for Long-Horizon Coding Agents — autocompaction that cuts coding-agent cost up to 50% inside a bounded window without losing Terminal-Bench performance.
    • Compacted context is only truncated or dropped, never rephrased, and a compaction is never compacted again.
    • KernelBench: CUDA kernel speedups of 2.23× after 200 steps, 3.58× after 400.
    • Under test-time scaling it adds >10 points for less than the cost of two full-context runs.
    • Scaffold-agnostic API-proxy released; Tim Dettmers is a co-author. (arXiv cs.AI · submitted Sep 22)
  • DeepSeek Elastic Compute (DSec): A Sandbox Infrastructure for Effective Agentic Training at Scale — the production report behind DeepSeek’s agentic RL: one unit is ~160 nodes serving ~3M sandboxes a day.
    • 380,000+ concurrent sandboxes and >5,000 creations per second, across FnCall, container, microVM and full-VM backends.
    • Stateful rollout is decoupled from preemptible GPU training, so rollout state survives while idle resources are reclaimed.
    • Agent misbehaviour like reward hacking is explicitly mitigated, not just measured.
    • Flag: systems report, no code release; submitted Sep 19, surfaced on lobste.rs today. (arXiv cs.DC)
  • A2M: Trace-Optimized Agent Hijacking in the MCP Ecosystem — MCP’s semantic tool selection is an attacker-controlled supply chain; the attack is black-box and needs no access to the agent.
    • Attraction optimizes tool metadata to raise invocation probability; Manipulation reads execution traces to craft steering tool returns.
    • Against GLM-4.6 on LiveMCPBench: 93.6% malicious tool-invocation rate across four scenarios.
    • 74.4% mean success on exfiltration, environment integrity and reasoning derailment; token cost inflated 32.4× under a cognitive-DoS variant.
    • Transfers to four other models with no re-optimization; code released. (arXiv cs.CR · AACL-IJCNLP 2026)
  • Measuring the Serving Stack Instead of the Model: Hidden Confounds in Local Tool-Use Evaluation — your tool-call fidelity numbers may be measuring Ollama, not the model.
    • Ollama gates the default tools= request per model by a static template flag; call format differs by model.
    • Phi-3 and Gemma-3 are rejected before inference runs, which naive harnesses log as 0% fidelity.
    • Ollama, llama.cpp, vLLM and SGLang each handle the same request differently; turn-pooled vs per-instance estimates differ by up to ~55 points. (arXiv cs.CL · REALM @ EMNLP 2026)
  • Writing Rust code that’s faster than state-of-the-art libraries by asking agents to make the code faster — an agentic optimization loop with guardrails beats hand-tuned SOTA libraries by 1.5-2× per model generation.
    • ~7.5-32× cumulative over the first baseline; an agent-optimized GBDT beats xgboost on speed, sometimes on quality.
    • What worked: a pass/fail target (“every CPU benchmark ≥1.2× faster than the true baseline”), a ban on touching benchmarks, iterate-until-convergence.
    • Flag: the author’s own caveat is benchmaxxing unless the suite is heterogeneous; the optimized crates are not released. (minimaxir.com · lobste.rs)

Models & research

  • Recursive self-improvement of AI research agents — a self-improvement loop run for real: AIDE² edits its own code and keeps the rewrites that win on hidden evals.
    • An autonomous 8-day run produced seven successive improvements, from a new search policy to context-compressing memory.
    • Gains transfer to four held-out benchmarks, including out-of-distribution weather forecasting.
    • On all four, the strongest discovered agent matches or beats a human-engineered production research agent.
    • Unoptimized side effect: reward hacking on a separate held-out family fell from 55% to 32%. (arXiv cs.AI · Sep 22 · 28 pages)
  • The current balance of power in open models — Nathan Lambert’s written Congressional testimony is the cleanest open-weights scoreboard right now.
    • China’s Hugging Face download lead is ~1.6B, twice the US total.
    • Every top US open model sits behind 15 Chinese ones on the AA index — GLM-5.3 45 and Kimi K3 44 vs Inkling 26.
    • His estimate: Chinese open weights trail the closed US frontier by 2-5 months; US open weights by 6-9.
    • Contrarian bit: full anti-distillation KYC would widen the gap by only 1-2 months. (HN 93 · 34c)

Policy & provenance

  • Inside the US ‘kill chain’ that destroyed an Iranian school — Pentagon investigators traced a strike that killed 123 children in Minab to flawed intelligence, outdated imagery and overreliance on AI.
    • A companion Bloomberg story reports the military has since modified its AI combat-targeting procedures.
    • Blumenthal on the delay: “The administration should have been forthcoming within hours or days and here we are months later… It is unforgivable.”
    • Flag: the page renders its lede only — the body sits behind Bloomberg’s subscription wall at check time. (HN 673 · 347c)
  • Continued: the AI czar gets a name — Bessent frontrunner, and a US-China incident channel — day 5 of coverage (base specs in the 2026-09-20 digest).
    • Three sources put Treasury Secretary Scott Bessent in front; OSTP’s Kratsios, OPM’s Kupor and National Cyber Director Sean Cairncross are also in the mix.
    • Cairncross clashed with Bessent over Anthropic’s Mythos in May.
    • Bessent met Vice Premier He Lifeng on the UNGA sidelines this week and discussed a US-China “notification mechanism” for AI incidents that threaten national security, ahead of the Trump-Xi meeting.
    • The White House calls unannounced personnel reporting “baseless speculation”; Treasury did not comment. (Semafor · Techmeme)
All gathered items - what was cut and why (26)